18 citations · 19 across the 5 of their papers we have counts for
3 papers · 1 filter
Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science
Zilinghan Li, Aditya Sinha, Yijiang Li +3
Federated learning (FL) is a promising approach to enabling collaborative model training without centralized data sharing, a crucial requirement in scientific domains where data pr…
A Terminology for Scientific Workflow Systems
Frédéric Suter, Tainã Coleman, İlkay Altintaş +23
The term scientific workflow has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become…
Lossy Compression of Scientific Data: Applications Constrains and Requirements
Franck Cappello, Allison Baker, Ebru Bozda +22
Increasing data volumes from scientific simulations and instruments (supercomputers, accelerators, telescopes) often exceed network, storage, and analysis capabilities. The scienti…